Identifying local features or points of interest in an electronic image is a component of many approaches for computerized object recognition, object detection, image matching, and 3D reconstruction. In these scenarios, a common approach is to use interest point detectors to estimate a reduced set of local image regions that are relatively invariant to occlusion, orientation, illumination and viewpoint changes. An interest point operator can define these regions by spatial locations, orientations, scales and possibly affine transformations. Descriptors can be computed from these image regions to find reliable image-to-image or image-to-model matches. Interest point detectors may have properties such as: (1) the interest points are repeatable, (2) the descriptors produced are individually identifiable and (3) the interest points are well-distributed spatially and across scales.
An interest point can be defined based on some function of the image, typically a series of filtering operations followed by extrema detection. Some example techniques that work based on this principle are the Harris corner detector, the Difference of Gaussian DoG (Difference of Gaussians) detector, the Laplacian detector, and their variants including Harris-Laplace and Hessian-Laplace detectors. Detectors that find affine co-variant features have also been proposed such as Harris-affine, Hessian-affine, Maximally Stable Extremal Regions (MSER) and salient regions. Most of these approaches perform a series of linear filtering operations on the image's intensities to detect interest point positions. However, filtering intensities directly can result in reduced repeatability for finding interest points in an image under non-linear lighting variations that commonly occur in real world scenarios. Furthermore, when detecting objects in a scene, changes in the background can also result in non-linear intensity variations along object boundaries, resulting in a similar reduction in repeatability for finding interest points in an image.